Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	26,019
2	Arizona	24,661
3	Florida	22,901
4	New York	21,669
5	New Jersey	20,770
6	Mississippi	20,538
7	Alabama	18,898
8	Rhode Island	18,167
9	South Carolina	18,053
10	District of Columbia	17,446
11	Massachusetts	17,215
12	Georgia	16,907
13	Nevada	16,634
14	Texas	15,929
15	Tennessee	15,799
16	Delaware	15,460
17	Maryland	15,168
18	Arkansas	14,777
19	Illinois	14,618
20	Iowa	14,549
21	Connecticut	14,041
22	Nebraska	13,935
23	California	13,217
24	Utah	12,966
25	Idaho	12,220
26	North Carolina	12,090
27	Virginia	10,908
28	Indiana	10,414
29	Wisconsin	10,214
30	South Dakota	10,196
31	New Mexico	10,077
32	Minnesota	10,036
33	Kansas	10,022
34	Oklahoma	9,751
35	Pennsylvania	9,290
36	Michigan	9,262
37	North Dakota	8,908
38	Missouri	8,741
39	Colorado	8,355
40	Ohio	8,038
41	Washington	8,012
42	Kentucky	7,274
43	Puerto Rico	5,883
44	Alaska	5,552
45	Wyoming	4,920
46	New Hampshire	4,898
47	Oregon	4,598
48	Montana	3,975
49	West Virginia	3,890
50	Maine	2,953
51	Vermont	2,286
52	Hawaii	1,712

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Alabama	335
2	Nevada	334
3	Florida	333
4	Louisiana	327
5	Mississippi	266
6	Georgia	256
7	South Carolina	254
8	Arizona	247
9	Tennessee	232
10	Texas	232
11	Arkansas	230
12	Puerto Rico	209
13	Idaho	183
14	Oklahoma	179
15	Alaska	176
16	California	168
17	Maryland	154
18	Missouri	141
19	Wisconsin	140
20	North Carolina	138
21	North Dakota	138
22	Utah	137
23	Nebraska	128
24	Virginia	125
25	Kansas	123
26	Minnesota	123
27	Iowa	121
28	Illinois	119
29	Indiana	114
30	Kentucky	113
31	Washington	100
32	South Dakota	96
33	Delaware	91
34	District of Columbia	88
35	Montana	84
36	New Mexico	84
37	Hawaii	79
38	Ohio	79
39	Rhode Island	70
40	Wyoming	70
41	Oregon	69
42	Colorado	67
43	West Virginia	61
44	Michigan	58
45	Pennsylvania	55
46	Massachusetts	50
47	New Jersey	35
48	New York	31
49	Connecticut	23
50	New Hampshire	18
51	Maine	14
52	Vermont	6

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,784
2	New York	1,666
3	Massachusetts	1,254
4	Connecticut	1,244
5	Rhode Island	953
6	Louisiana	865
7	District of Columbia	830
8	Michigan	647
9	Illinois	609
10	Delaware	600
11	Maryland	582
12	Mississippi	574
13	Pennsylvania	568
14	Arizona	520
15	Indiana	442
16	Georgia	354
17	South Carolina	348
18	Alabama	333
19	Florida	333
20	Colorado	321
21	New Mexico	312
22	New Hampshire	306
23	Ohio	302
24	Minnesota	293
25	Iowa	280
26	Nevada	274
27	Texas	265
28	Virginia	259
29	California	240
30	Washington	221
31	Missouri	214
32	North Carolina	191
33	Nebraska	172
34	Kentucky	170
35	Wisconsin	164
36	Arkansas	157
37	Tennessee	157
38	South Dakota	152
39	North Dakota	143
40	Oklahoma	139
41	Kansas	125
42	Idaho	113
43	Utah	98
44	Maine	92
45	Vermont	91
46	Oregon	79
47	Puerto Rico	72
48	West Virginia	65
49	Montana	59
50	Wyoming	46
51	Alaska	31
52	Hawaii	17

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Louisiana	5
2	Mississippi	5
3	South Carolina	5
4	Arizona	4
5	Florida	4
6	Texas	4
7	Alabama	3
8	Arkansas	2
9	California	2
10	Georgia	2
11	New Mexico	2
12	North Carolina	2
13	Idaho	1
14	Iowa	1
15	Maryland	1
16	Massachusetts	1
17	Montana	1
18	Nevada	1
19	New Jersey	1
20	Ohio	1
21	Puerto Rico	1
22	South Dakota	1
23	Tennessee	1
24	Virginia	1
25	Washington	1
26	Alaska	0
27	Colorado	0
28	Connecticut	0
29	Delaware	0
30	District of Columbia	0
31	Hawaii	0
32	Illinois	0
33	Indiana	0
34	Kansas	0
35	Kentucky	0
36	Maine	0
37	Michigan	0
38	Minnesota	0
39	Missouri	0
40	Nebraska	0
41	New Hampshire	0
42	New York	0
43	North Dakota	0
44	Oklahoma	0
45	Oregon	0
46	Pennsylvania	0
47	Rhode Island	0
48	Utah	0
49	Vermont	0
50	West Virginia	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	139,312	1	99
Lake	Tennessee	106,756	2	99
Lee	Arkansas	100,485	3	99
Dakota	Nebraska	94,927	4	99
Lincoln	Arkansas	91,063	5	99
Davidson	Tennessee	31,362	120	96
Richland	South Carolina	19,338	380	87
Orange	California	11,907	861	72
York	South Carolina	11,552	888	71
Pierce	Washington	6,210	1622	48

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,020	1	99
Randolph	Georgia	3,836	2	99
Terrell	Georgia	3,517	3	99
Galax city	Virginia	3,466	4	99
McKinley	New Mexico	3,097	5	99
Richland	South Carolina	339	658	79
Davidson	Tennessee	291	759	75
Orange	California	205	987	68
Pierce	Washington	151	1215	61
York	South Carolina	100	1498	52

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.